A detailed investigation of classification methods for vowel speech imagery recognition

Accurate and fast decoding of speech imagery from electroencephalographic (EEG) data could serve as a basis for a new generation of brain computer interfaces (BCIs), more portable and easier to use. However, decoding of speech imagery from EEG is a hard problem due to many factors. In this paper we focus on the analysis of the classification step of speech imagery decoding for a three-class vowel speech imagery recognition problem. We empirically show that different classification subtasks may require different classifiers for accurately decoding and obtain a classification accuracy that improves the best results previously published. We further investigate the relationship between the classifiers and different sets of features selected by the common spatial patterns method. Our results indicate that further improvement on BCIs based on speech imagery could be achieved by carefully selecting an appropriate combination of classifiers for the subtasks involved.

Preliminary investigation on growth responses of Cyprinus carpio L. fed on locally formulated artificial diets

The growth rate and feed conversion ratios of the common carp, Cyprinus carpio were measured for five test diets in 14-day replicate laboratory studies. The young carp were fed with artificial test diets with crude protein contents ranging from 14.50 to 21.42 per cent. Within this range of feed characteristic optimum growth rates were obtained with diets containing 20.25 and 21.42 per cent crude protein. The study of the effect of varying ration levels showed that growth rates increased with increases of ration size, but the food conversion efficiency and protein efficiency ratios decreased markedly as ration size was increased